

"""simple docstring"""






import os
from pathlib import Path
def    UpperCamelCase       (      _lowerCAmelCase  :    Any,	_lowerCAmelCase  :    Dict,	_lowerCAmelCase  :    Tuple,	_lowerCAmelCase  :    List[Any]			)					->      Union[str, Any]:
	_UpperCAmelCase     :				List[Any]          =       {
	    """en""": """Machine learning is great, isn't it?""",
	    """ru""": """Машинное обучение - это здорово, не так ли?""",
	    """de""": """Maschinelles Lernen ist großartig, nicht wahr?""",
	}

	# BLUE scores as follows:
	# "pair": [fairseq, transformers]
	_UpperCAmelCase     :				Union[str, Any]          =       {
	    """wmt16-en-de-dist-12-1""": [28.3, 27.52],
	    """wmt16-en-de-dist-6-1""": [27.4, 27.11],
	    """wmt16-en-de-12-1""": [26.9, 25.75],
	}
	_UpperCAmelCase     :				Any          =       f'''{src_lang}-{tgt_lang}'''

	_UpperCAmelCase     :				Dict          =       f'''
---
language:
- {src_lang}
- {tgt_lang}
thumbnail:
tags:
- translation
- wmt16
- allenai
license: apache-2.0
datasets:
- wmt16
metrics:
- bleu
---

# FSMT

## Model description

This is a ported version of fairseq-based [wmt16 transformer](https://github.com/jungokasai/deep-shallow/) for {src_lang}-{tgt_lang}.

For more details, please, see [Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation](https://arxiv.org/abs/2006.10369).

All 3 models are available:

* [wmt16-en-de-dist-12-1](https://huggingface.co/allenai/wmt16-en-de-dist-12-1)
* [wmt16-en-de-dist-6-1](https://huggingface.co/allenai/wmt16-en-de-dist-6-1)
* [wmt16-en-de-12-1](https://huggingface.co/allenai/wmt16-en-de-12-1)


## Intended uses & limitations

#### How to use

```python
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
mname = "allenai/{model_name}"
tokenizer = FSMTTokenizer.from_pretrained(mname)
model = FSMTForConditionalGeneration.from_pretrained(mname)

input = "{texts[src_lang]}"
input_ids = tokenizer.encode(input, return_tensors="pt")
outputs = model.generate(input_ids)
decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(decoded) # {texts[tgt_lang]}

```

#### Limitations and bias


## Training data

Pretrained weights were left identical to the original model released by allenai. For more details, please, see the [paper](https://arxiv.org/abs/2006.10369).

## Eval results

Here are the BLEU scores:

model   | fairseq | transformers
-------|---------|----------
{model_name}  | {scores[model_name][0]} | {scores[model_name][1]}

The score is slightly below the score reported in the paper, as the researchers don\'t use `sacrebleu` and measure the score on tokenized outputs. `transformers` score was measured using `sacrebleu` on detokenized outputs.

The score was calculated using this code:

```bash
git clone https://github.com/huggingface/transformers
cd transformers
export PAIR={pair}
export DATA_DIR=data/$PAIR
export SAVE_DIR=data/$PAIR
export BS=8
export NUM_BEAMS=5
mkdir -p $DATA_DIR
sacrebleu -t wmt16 -l $PAIR --echo src > $DATA_DIR/val.source
sacrebleu -t wmt16 -l $PAIR --echo ref > $DATA_DIR/val.target
echo $PAIR
PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py allenai/{model_name} $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS
```

## Data Sources

- [training, etc.](http://www.statmt.org/wmt16/)
- [test set](http://matrix.statmt.org/test_sets/newstest2016.tgz?1504722372)


### BibTeX entry and citation info

```
@misc{{kasai2020deep,
    title={{Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation}},
    author={{Jungo Kasai and Nikolaos Pappas and Hao Peng and James Cross and Noah A. Smith}},
    year={{2020}},
    eprint={{2006.10369}},
    archivePrefix={{arXiv}},
    primaryClass={{cs.CL}}
}}
```

'''
	model_card_dir.mkdir(parents=__lowerCAmelCase,	exist_ok=__lowerCAmelCase			)
	_UpperCAmelCase     :				Tuple          =       os.path.join(__lowerCAmelCase,	"""README.md"""			)
	print(f'''Generating {path}'''			)
	with open(__lowerCAmelCase,	"""w""",	encoding="""utf-8"""			) as f:
		f.write(__lowerCAmelCase			)


# make sure we are under the root of the project
lowerCamelCase__					:	List[Any]				    =	Path(__file__).resolve().parent.parent.parent
lowerCamelCase__					:	Tuple				    =	repo_dir / '''model_cards'''

for model_name in ["wmt16-en-de-dist-12-1", "wmt16-en-de-dist-6-1", "wmt16-en-de-12-1"]:
						lowerCamelCase__					:	str				    =	model_cards_dir / '''allenai''' / model_name
						write_model_card(model_card_dir, src_lang='''en''', tgt_lang='''de''', model_name=model_name)






"""simple docstring"""


def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase							)				->   float:




						'''simple docstring'''



						return price * (1 + tax_rate)


if __name__ == "__main__":
	print(F'''{price_plus_tax(100, 0.25) = }''')
	print(F'''{price_plus_tax(125.50, 0.05) = }''')


